As discussed in the survey [1]}, OOD detection falls into the category of unintentional anomaly detection.
Given a pretrained DNN model, Hendrycks analyzed that the softmax value could statistically distinguish in-distribution and OOD data [2]}.
Based on this observation, they used softmax value as a baseline for OOD detection.
ODIN [3]} improved the baseline with two strategies, , temperature scaling and input preprocessing, to further distinguish in-distribution and OOD data.
